


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					field(default="question-answering-extractive"  ,  metadata={"include_in_asdict_even_if_is_default": True}					)
					__UpperCAmelCase							  =					Features({"question": Value("string"					), "context": Value("string"					)}					)
					__UpperCAmelCase							  =					Features(
					    {
					        "answers": Sequence(
					            {
					                "text": Value("string"					),
					                "answer_start": Value("int32"					),
					            }					)
					    }					)
					__UpperCAmelCase							  =					"question"
					__UpperCAmelCase							  =					"context"
					__UpperCAmelCase							  =					"answers"






					@property
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


